6-mult

set.seed(1234)
x1 <- rnorm(1000, 0, 1)
x2 <- sample(c(rnorm(500, -3, 1), rnorm(500, 3, 1)), size = 1000)
x7 <- sample(c(rnorm(500, -5, 1), rnorm(500, 5, 1)), size = 1000)
x8 <- rnorm(1000, 0, 1)
x9 <- rnorm(1000, 0, 1)
x10 <- rnorm(1000, 0, 1)

data_mult <- cbind(x1, x2, x7, x8, x9, x10)

origin_dt_bi <- data_mult %>% as_tibble() %>% 
  gather(names, values) %>%
  mutate(names = as_factor(names),
         names = fct_relevel(names, levels = c("x1", "x2", "x7","x8", "x9", "x10")))

origin_dt_bi %>% 
  ggplot(aes(x = values)) +
  geom_histogram(binwidth = 0.5) +
  geom_density(aes(y = 0.5 * ..count..)) +
  facet_wrap(vars(names), ncol = 3)

col <- data_mult %>% as_tibble() %>% 
  transmute(col1 = ifelse(x2 < 0, "left", "right"), 
                     col2 = ifelse(x7 < 0, "left", "right"))

as_tibble(data_mult) %>% bind_cols(col) %>%  
  ggplot(aes(x = x2, y = x7)) + 
  geom_point()

search_geodesic()

proj <- mult_geodesic %>% 
  filter(info == "interpolation") %>% pull(basis) %>% tail(1)
proj[[1]]

##              [,1]        [,2]
## [1,] -0.001738144  0.03945460
## [2,]  0.496024430 -0.86384416
## [3,]  0.862833666  0.49212418
## [4,] -0.028429470 -0.05467946
## [5,]  0.077831028  0.06522615
## [6,] -0.051077434  0.05278356

projected <- scale(data_mult) %*% proj[[1]] %>% as_tibble()
projected %>% 
  ggplot(aes(x = V1, y = V2)) + 
  geom_point() + 
  ggtitle("without rounding")

PCA plot for V1

# colour by parts of optimisation (static)
mult_releveled <- pca_v1 %>%
  filter(info != "interpolation") 

p1 <- pca_v1 %>% 
  ggplot(aes(x = PC1, y = PC2, col = info)) +
  geom_point() +
  geom_point(data = mult_releveled) +
  theme(aspect.ratio = 1) + 
  ggtitle("PCA plot for v1")


# colour by parts of optimisation (static)
mult_releveled <- pca_v2 %>%
  filter(info != "interpolation") 

p2 <- pca_v2 %>% 
  ggplot(aes(x = PC1, y = PC2, col = info)) +
  geom_point() +
  geom_point(data = mult_releveled) +
  theme(aspect.ratio = 1) + 
  ggtitle("PCA plot for v2")

p1 + p2 + plot_layout(guides = "collect")

mult_animate_v1 <- pca_v1 %>%
  mutate(loop = ifelse(info == "start", 1, loop)) %>% 
  group_by(tries, loop, info3) %>%
  mutate(animate_id = group_indices())

mult_animate_v2 <- pca_v2 %>%
  mutate(loop = ifelse(info == "start", 1, loop)) %>% 
  group_by(tries, loop, info3) %>%
  mutate(animate_id = group_indices())

gg_color_hue <- function(n) {
  hues = seq(15, 375, length = n + 1)
  hcl(h = hues, l = 65, c = 100)[1:n]
}

mult_path_v1 <- mult_animate_v1 %>% 
  ungroup() %>% 
  ggplot(aes(x = animate_id, y = index_val, col = info)) + 
  geom_point() + 
  theme(legend.position =  "none")

mult_path_v2 <- mult_animate_v2 %>% 
  ungroup() %>% 
  ggplot(aes(x = animate_id, y = index_val, col = info)) + 
  geom_point() 


last_tries_v1 <- mult_animate_v1$tries[which.max(mult_animate_v1$tries)]
last_tries_v2 <- mult_animate_v2$tries[which.max(mult_animate_v2$tries)]

direction_v1 <- mult_animate_v1 %>% 
  filter(tries == last_tries_v1, info %in% c("direction_search", "best_direction_search")) %>%
  ggplot(aes(x = animate_id, y = index_val, col = info)) + 
  geom_point() + 
  xlim(1, max(mult_animate_v1$animate_id)) + 
  scale_color_manual(values = c(gg_color_hue(6)[2:3], gg_color_hue(6)[5:6])) + 
  theme(legend.position =  "none")

direction_v2 <- mult_animate_v2 %>% 
  filter(tries == last_tries_v2, info %in% c("direction_search", "best_direction_search")) %>%
  ggplot(aes(x = animate_id, y = index_val, col = info)) + 
  geom_point() + 
  xlim(1, max(mult_animate_v2$animate_id)) + 
  scale_color_manual(values = c(gg_color_hue(6)[2:3], gg_color_hue(6)[5:6])) + 
  theme(legend.position =  "none")
  

design <- "
  13
  24
  24
"
direction_v1 + mult_path_v1 + direction_v2 + mult_path_v2 +
  plot_layout(design = design,  guides = "collect")

geo_animate <- mult_animate_v1 %>%
  ggplot(aes(x = PC1, y = PC2, col = info)) +
  geom_point() +
  theme(aspect.ratio = 1, 
        legend.position = "none") +
  gganimate::transition_time(animate_id) +
  gganimate::shadow_mark()

p1 <- gganimate::animate(geo_animate, fps = 2)

index_val_animate <- mult_animate_v1 %>% 
  ggplot(aes(x = animate_id, y = index_val, col = info)) + 
  geom_jitter(width = 0.0005) + 
  theme(aspect.ratio = 1) +
  gganimate::transition_time(animate_id) + 
  gganimate::shadow_mark()

p2 <- gganimate::animate(index_val_animate, fps = 2)

a_mgif <- image_read(p1)
b_mgif <- image_read(p2)

new_gif <- image_append(c(a_mgif[1], b_mgif[1]))
for (i in 2:100) {
  combined <- image_append(c(a_mgif[i], b_mgif[i]))
  new_gif <- c(new_gif, combined)
}

new_gif

geo_animate <- mult_animate_v2 %>%
  ggplot(aes(x = PC1, y = PC2, col = info)) +
  geom_point() +
  theme(aspect.ratio = 1, 
        legend.position = "none") +
  gganimate::transition_time(animate_id) +
  gganimate::shadow_mark()

p1 <- gganimate::animate(geo_animate, fps = 2)

index_val_animate <- mult_animate_v2 %>% 
  ggplot(aes(x = animate_id, y = index_val, col = info)) + 
  geom_jitter(width = 0.0005) + 
  theme(aspect.ratio = 1) +
  gganimate::transition_time(animate_id) + 
  gganimate::shadow_mark()

p2 <- gganimate::animate(index_val_animate, fps = 2)

a_mgif <- image_read(p1)
b_mgif <- image_read(p2)

new_gif <- image_append(c(a_mgif[1], b_mgif[1]))
for (i in 2:100) {
  combined <- image_append(c(a_mgif[i], b_mgif[i]))
  new_gif <- c(new_gif, combined)
}

new_gif